CO2 emissions reduction Performance of China's HSR based on substitution effect and demand effect

IF 2.7 4区 工程技术 Q2 TRANSPORTATION SCIENCE & TECHNOLOGY Transportation Safety and Environment Pub Date : 2022-12-21 DOI:10.1093/tse/tdac060
Liying Wang, Ping Yin, Shangqing Liu
{"title":"CO2 emissions reduction Performance of China's HSR based on substitution effect and demand effect","authors":"Liying Wang, Ping Yin, Shangqing Liu","doi":"10.1093/tse/tdac060","DOIUrl":null,"url":null,"abstract":"\n As an important transportation infrastructure and transportation backbone in China, high-speed rail (HSR) plays a critical role in promoting the development of green and low-carbon transportation. Calculating the CO2 emissions reduction performance of HSR will be conducive to promote the CO2 emissions reduction work of the railway. Based on the Dalkic HSR CO2 emissions reduction performance model, by adjusting HSR CO2 emission factor (CEFHSR), annual times of departures (T) and other parameters, this study develops China HSR CO2 emissions reduction performance model. Taking the Beijing-Shanghai HSR as the research object, this study conducts a questionnaire survey to explore the substitution effect and demand effect of HSR on different transportation modes, collects data such as passenger volume, average electricity use, and annual times of departures of Beijing-Shanghai HSR in 2019, and calculates the CO2 emissions reduction performance of the Beijing-Shanghai HSR. This study has two main results: (1) Build China HSR CO2 emissions reduction performance model based on substitution effect and demand effect. (2) In 2019, the CO2 emissions of Beijing-Shanghai HSR is 2898 233.62t, the CO2 emissions reduction performance of Beijing-Shanghai HSR is 17 999 482.8t, the annual CO2 emissions of Beijing-Shanghai line in ‘No HSR’ case is as 7.2 times as in \" HSR\" case, and PKT of HSR is 10.2 g/pkm. Based on the research results, this study proposes three CO2 emissions reduction policy suggestions. This study would be helpful for further HSR CO2 emissions reduction research and departments related to railway transportation management to make CO2 emissions reduction policies.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":" ","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Safety and Environment","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1093/tse/tdac060","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TRANSPORTATION SCIENCE & TECHNOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

As an important transportation infrastructure and transportation backbone in China, high-speed rail (HSR) plays a critical role in promoting the development of green and low-carbon transportation. Calculating the CO2 emissions reduction performance of HSR will be conducive to promote the CO2 emissions reduction work of the railway. Based on the Dalkic HSR CO2 emissions reduction performance model, by adjusting HSR CO2 emission factor (CEFHSR), annual times of departures (T) and other parameters, this study develops China HSR CO2 emissions reduction performance model. Taking the Beijing-Shanghai HSR as the research object, this study conducts a questionnaire survey to explore the substitution effect and demand effect of HSR on different transportation modes, collects data such as passenger volume, average electricity use, and annual times of departures of Beijing-Shanghai HSR in 2019, and calculates the CO2 emissions reduction performance of the Beijing-Shanghai HSR. This study has two main results: (1) Build China HSR CO2 emissions reduction performance model based on substitution effect and demand effect. (2) In 2019, the CO2 emissions of Beijing-Shanghai HSR is 2898 233.62t, the CO2 emissions reduction performance of Beijing-Shanghai HSR is 17 999 482.8t, the annual CO2 emissions of Beijing-Shanghai line in ‘No HSR’ case is as 7.2 times as in " HSR" case, and PKT of HSR is 10.2 g/pkm. Based on the research results, this study proposes three CO2 emissions reduction policy suggestions. This study would be helpful for further HSR CO2 emissions reduction research and departments related to railway transportation management to make CO2 emissions reduction policies.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于替代效应和需求效应的中国高铁CO2减排绩效
高铁作为我国重要的交通基础设施和交通骨干,在推动绿色低碳交通发展方面发挥着重要作用。计算高铁的二氧化碳减排绩效将有助于推动铁路的二氧化碳减排工作。本研究在Dalkic高铁CO2减排绩效模型的基础上,通过调整高铁CO2排放因子(CEFHSR)、年发车次数(T)等参数,建立了中国高铁CO2的减排绩效模型。本研究以京沪高铁为研究对象,进行问卷调查,探讨高铁对不同交通方式的替代效应和需求效应,收集2019年京沪高铁客运量、平均用电量、年发车次数等数据,并对京沪高铁的CO2减排性能进行了计算。本研究主要有两个结果:(1)建立了基于替代效应和需求效应的中国高铁CO2减排绩效模型。(2) 2019年,京沪高铁CO2排放量为2898 233.62t,京沪高铁CO2减排绩效为17999 482.8t,京沪线在“无高铁”情况下的年CO2排放量是“高铁”的7.2倍,高铁PKT为10.2 g/pkm。基于研究结果,本研究提出了三点CO2减排政策建议。本研究将有助于进一步开展高铁CO2减排研究和铁路运输管理相关部门制定CO2减排政策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Transportation Safety and Environment
Transportation Safety and Environment TRANSPORTATION SCIENCE & TECHNOLOGY-
CiteScore
3.90
自引率
13.60%
发文量
32
审稿时长
10 weeks
期刊最新文献
Parking choice behavior analysis of rural residents based on latent variable random forest model Risk Mapping of Wildlife-Vehicle Collisions across the State of Montana, U.S.A.: A Machine Learning Approach for Imbalanced Data along Rural Roads Evolutionary game analysis of the shared parking market promotion under government management The Characteristics of Driver Lane-Changing Behavior in Congested Road Environments Effect of helmet wearing regulation on electric bike riders: a case study of two cities in China
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1